scirs2-metrics 0.3.2

//! Float-time adaptation engine for neuromorphic systems
//!
//! This module provides online learning algorithms, continual learning strategies,
//! and dynamic architecture modification for real-time adaptation.

#![allow(clippy::too_many_arguments)]
#![allow(dead_code)]

use crate::error::Result;
use scirs2_core::numeric::Float;
use std::collections::HashMap;

/// Float-time adaptation engine
#[derive(Debug)]
pub struct RealtimeAdaptationEngine<F: Float> {
    /// Online learning algorithms
    pub online_learners: Vec<OnlineLearningAlgorithm<F>>,
    /// Continual learning strategies
    pub continual_learning: ContinualLearningSystem<F>,
    /// Catastrophic forgetting prevention
    pub forgetting_prevention: ForgettingPreventionSystem<F>,
    /// Dynamic architecture modification
    pub architecture_modifier: DynamicArchitectureModifier<F>,
    /// Float-time performance monitoring
    pub realtime_monitor: RealtimePerformanceMonitor<F>,
}

/// Online learning algorithm
#[derive(Debug)]
pub struct OnlineLearningAlgorithm<F: Float> {
    /// Algorithm type
    pub algorithm_type: OnlineLearningType,
    /// Learning parameters
    pub parameters: HashMap<String, F>,
    /// Current state
    pub state: OnlineLearningState<F>,
}

/// Types of online learning algorithms
#[derive(Debug, Clone)]
pub enum OnlineLearningType {
    /// Stochastic gradient descent
    StochasticGradientDescent,
    /// Online perceptron
    OnlinePerceptron,
    /// Adaptive algorithms (AdaGrad, Adam, etc.)
    AdaptiveAlgorithm(String),
    /// Evolutionary strategies
    EvolutionaryStrategy,
}

/// Online learning state
#[derive(Debug)]
pub struct OnlineLearningState<F: Float> {
    /// Current weights/parameters
    pub parameters: Vec<F>,
    /// Momentum terms
    pub momentum: Vec<F>,
    /// Adaptive learning rates
    pub adaptive_rates: Vec<F>,
    /// Step count
    pub step_count: usize,
}

/// Continual learning system
#[derive(Debug)]
pub struct ContinualLearningSystem<F: Float> {
    /// Elastic weight consolidation
    pub ewc: ElasticWeightConsolidation<F>,
    /// Progressive neural networks
    pub progressive_networks: ProgressiveNeuralNetworks<F>,
    /// Memory replay systems
    pub replay_systems: Vec<MemoryReplaySystem<F>>,
    /// Task-specific modules
    pub task_modules: HashMap<String, TaskSpecificModule<F>>,
}

/// Elastic weight consolidation for preventing catastrophic forgetting
#[derive(Debug)]
pub struct ElasticWeightConsolidation<F: Float> {
    /// Fisher information matrix
    pub fisher_information: Vec<F>,
    /// Important weights from previous tasks
    pub important_weights: Vec<F>,
    /// Regularization strength
    pub lambda: F,
}

/// Progressive neural networks
#[derive(Debug)]
pub struct ProgressiveNeuralNetworks<F: Float> {
    /// Task-specific columns
    pub task_columns: Vec<TaskColumn<F>>,
    /// Lateral connections between columns
    pub lateral_connections: HashMap<(usize, usize), LateralConnection<F>>,
    /// Adapter modules
    pub adapters: Vec<AdapterModule<F>>,
}

/// Task-specific column in progressive networks
#[derive(Debug)]
pub struct TaskColumn<F: Float> {
    /// Column ID
    pub id: usize,
    /// Task identifier
    pub task_id: String,
    /// Column parameters
    pub parameters: Vec<F>,
    /// Activation function type
    pub activation_type: String,
}

/// Lateral connection between columns
#[derive(Debug)]
pub struct LateralConnection<F: Float> {
    /// Connection weights
    pub weights: Vec<F>,
    /// Connection type
    pub connection_type: String,
}

/// Adapter module for cross-task transfer
#[derive(Debug)]
pub struct AdapterModule<F: Float> {
    /// Adapter parameters
    pub parameters: Vec<F>,
    /// Input dimension
    pub input_dim: usize,
    /// Output dimension
    pub output_dim: usize,
}

/// Memory replay system
#[derive(Debug)]
pub struct MemoryReplaySystem<F: Float> {
    /// Replay buffer
    pub replay_buffer: ReplayBuffer<F>,
    /// Replay strategy
    pub replay_strategy: ReplayStrategy,
    /// Replay frequency
    pub replay_frequency: usize,
}

/// Replay buffer for storing past experiences
#[derive(Debug)]
pub struct ReplayBuffer<F: Float> {
    /// Buffer capacity
    pub capacity: usize,
    /// Stored experiences
    pub experiences: Vec<Experience<F>>,
    /// Current index for circular buffer
    pub current_index: usize,
}

/// Experience stored in replay buffer
#[derive(Debug)]
pub struct Experience<F: Float> {
    /// Input data
    pub input: Vec<F>,
    /// Target output
    pub target: Vec<F>,
    /// Task identifier
    pub task_id: String,
    /// Importance weight
    pub importance: F,
}

/// Replay strategy
#[derive(Debug, Clone)]
pub enum ReplayStrategy {
    /// Random sampling
    Random,
    /// Prioritized replay based on importance
    Prioritized,
    /// Reservoir sampling
    Reservoir,
    /// Gradient-based selection
    GradientBased,
}

/// Task-specific module
#[derive(Debug)]
pub struct TaskSpecificModule<F: Float> {
    /// Module parameters
    pub parameters: Vec<F>,
    /// Task identifier
    pub task_id: String,
    /// Module type
    pub module_type: String,
}

/// Forgetting prevention system
#[derive(Debug)]
pub struct ForgettingPreventionSystem<F: Float> {
    /// Regularization methods
    pub regularization_methods: Vec<RegularizationMethod<F>>,
    /// Memory consolidation strategies
    pub consolidation_strategies: Vec<ConsolidationStrategy<F>>,
    /// Importance estimation
    pub importance_estimator: ImportanceEstimator<F>,
}

/// Regularization method for preventing forgetting
#[derive(Debug)]
pub struct RegularizationMethod<F: Float> {
    /// Method type
    pub method_type: String,
    /// Regularization strength
    pub strength: F,
    /// Target parameters
    pub target_parameters: Vec<F>,
}

/// Consolidation strategy
#[derive(Debug)]
pub struct ConsolidationStrategy<F: Float> {
    /// Strategy type
    pub strategy_type: String,
    /// Consolidation parameters
    pub parameters: HashMap<String, F>,
}

/// Importance estimator for parameters
#[derive(Debug)]
pub struct ImportanceEstimator<F: Float> {
    /// Estimation method
    pub method: String,
    /// Current importance scores
    pub importance_scores: Vec<F>,
}

impl<F: Float> ImportanceEstimator<F> {
    pub fn new() -> Self {
        Self {
            method: "gradient_based".to_string(),
            importance_scores: Vec::new(),
        }
    }
}

/// Dynamic architecture modifier
#[derive(Debug)]
pub struct DynamicArchitectureModifier<F: Float> {
    /// Architecture growth strategies
    pub growth_strategies: Vec<GrowthStrategy<F>>,
    /// Pruning strategies
    pub pruning_strategies: Vec<PruningStrategy<F>>,
    /// Architecture optimization
    pub optimization_strategies: Vec<ArchitectureOptimization<F>>,
}

/// Architecture growth strategy
#[derive(Debug)]
pub struct GrowthStrategy<F: Float> {
    /// Growth type
    pub growth_type: String,
    /// Growth parameters
    pub parameters: HashMap<String, F>,
}

/// Pruning strategy
#[derive(Debug)]
pub struct PruningStrategy<F: Float> {
    /// Pruning type
    pub pruning_type: String,
    /// Pruning threshold
    pub threshold: F,
}

/// Architecture optimization strategy
#[derive(Debug)]
pub struct ArchitectureOptimization<F: Float> {
    /// Optimization type
    pub optimization_type: String,
    /// Optimization parameters
    pub parameters: HashMap<String, F>,
}

/// Float-time performance monitor
#[derive(Debug)]
pub struct RealtimePerformanceMonitor<F: Float> {
    /// Performance metrics
    pub metrics: HashMap<String, F>,
    /// Monitoring frequency
    pub monitoring_frequency: usize,
    /// Alert thresholds
    pub alert_thresholds: HashMap<String, F>,
}

impl<F: Float> RealtimeAdaptationEngine<F> {
    /// Create new real-time adaptation engine
    pub fn new() -> Result<Self> {
        Ok(Self {
            online_learners: Vec::new(),
            continual_learning: ContinualLearningSystem::new()?,
            forgetting_prevention: ForgettingPreventionSystem::new()?,
            architecture_modifier: DynamicArchitectureModifier::new()?,
            realtime_monitor: RealtimePerformanceMonitor::new()?,
        })
    }

    /// Adapt to new data in real-time
    pub fn adapt(&mut self, input: &[F], target: &[F]) -> Result<()> {
        // Update online learners
        for learner in &mut self.online_learners {
            learner.update(input, target)?;
        }

        // Check for forgetting and apply prevention
        self.forgetting_prevention
            .assess_and_prevent(input, target)?;

        // Consider architectural changes if needed
        if self.should_modify_architecture()? {
            self.architecture_modifier.modify_architecture()?;
        }

        // Update performance monitoring
        self.realtime_monitor.update_metrics(input, target)?;

        Ok(())
    }

    /// Check if architecture modification is needed
    fn should_modify_architecture(&self) -> Result<bool> {
        // Simple heuristic: modify if performance drops below threshold
        if let Some(&performance) = self.realtime_monitor.metrics.get("accuracy") {
            Ok(performance < F::from(0.8).expect("Failed to convert constant to float"))
        } else {
            Ok(false)
        }
    }
}

impl<F: Float> OnlineLearningAlgorithm<F> {
    /// Create new online learning algorithm
    pub fn new(algorithm_type: OnlineLearningType, parameters: HashMap<String, F>) -> Self {
        Self {
            algorithm_type,
            parameters,
            state: OnlineLearningState::new(),
        }
    }

    /// Update algorithm with new data
    pub fn update(&mut self, input: &[F], target: &[F]) -> Result<()> {
        match self.algorithm_type {
            OnlineLearningType::StochasticGradientDescent => {
                self.sgd_update(input, target)?;
            }
            OnlineLearningType::OnlinePerceptron => {
                self.perceptron_update(input, target)?;
            }
            _ => {
                // Default update
                self.default_update(input, target)?;
            }
        }
        self.state.step_count += 1;
        Ok(())
    }

    /// SGD update
    fn sgd_update(&mut self, input: &[F], target: &[F]) -> Result<()> {
        let learning_rate = self
            .parameters
            .get("learning_rate")
            .copied()
            .unwrap_or_else(|| F::from(0.01).expect("Failed to convert constant to float"));

        // Simplified SGD update
        for i in 0..self.state.parameters.len().min(input.len()) {
            let gradient = input[i]
                * (target.first().copied().unwrap_or(F::zero()) - self.state.parameters[i]);
            self.state.parameters[i] = self.state.parameters[i] + learning_rate * gradient;
        }
        Ok(())
    }

    /// Perceptron update
    fn perceptron_update(&mut self, input: &[F], target: &[F]) -> Result<()> {
        let learning_rate = self
            .parameters
            .get("learning_rate")
            .copied()
            .unwrap_or_else(|| F::from(0.01).expect("Failed to convert constant to float"));

        // Simplified perceptron update
        let prediction = self.predict(input)?;
        let error = target.first().copied().unwrap_or(F::zero()) - prediction;

        if error.abs() > F::from(0.001).expect("Failed to convert constant to float") {
            for i in 0..self.state.parameters.len().min(input.len()) {
                self.state.parameters[i] =
                    self.state.parameters[i] + learning_rate * error * input[i];
            }
        }
        Ok(())
    }

    /// Default update
    fn default_update(&mut self, _input: &[F], _target: &[F]) -> Result<()> {
        // Placeholder for other algorithms
        Ok(())
    }

    /// Make prediction
    pub fn predict(&self, input: &[F]) -> Result<F> {
        let mut sum = F::zero();
        for i in 0..self.state.parameters.len().min(input.len()) {
            sum = sum + self.state.parameters[i] * input[i];
        }
        Ok(sum)
    }
}

impl<F: Float> OnlineLearningState<F> {
    /// Create new online learning state
    pub fn new() -> Self {
        Self {
            parameters: Vec::new(),
            momentum: Vec::new(),
            adaptive_rates: Vec::new(),
            step_count: 0,
        }
    }

    /// Initialize parameters
    pub fn initialize(&mut self, size: usize) {
        self.parameters = vec![F::zero(); size];
        self.momentum = vec![F::zero(); size];
        self.adaptive_rates =
            vec![F::from(0.01).expect("Failed to convert constant to float"); size];
    }
}

impl<F: Float> ContinualLearningSystem<F> {
    /// Create new continual learning system
    pub fn new() -> Result<Self> {
        Ok(Self {
            ewc: ElasticWeightConsolidation::new(),
            progressive_networks: ProgressiveNeuralNetworks::new(),
            replay_systems: Vec::new(),
            task_modules: HashMap::new(),
        })
    }
}

impl<F: Float> ElasticWeightConsolidation<F> {
    /// Create new EWC system
    pub fn new() -> Self {
        Self {
            fisher_information: Vec::new(),
            important_weights: Vec::new(),
            lambda: F::from(1000.0).expect("Failed to convert constant to float"),
        }
    }
}

impl<F: Float> ProgressiveNeuralNetworks<F> {
    /// Create new progressive neural networks
    pub fn new() -> Self {
        Self {
            task_columns: Vec::new(),
            lateral_connections: HashMap::new(),
            adapters: Vec::new(),
        }
    }
}

// Placeholder implementations for supporting types
impl<F: Float> ForgettingPreventionSystem<F> {
    pub fn new() -> Result<Self> {
        Ok(Self {
            regularization_methods: Vec::new(),
            consolidation_strategies: Vec::new(),
            importance_estimator: ImportanceEstimator::new(),
        })
    }
}

impl<F: Float> DynamicArchitectureModifier<F> {
    pub fn new() -> Result<Self> {
        Ok(Self {
            growth_strategies: Vec::new(),
            pruning_strategies: Vec::new(),
            optimization_strategies: Vec::new(),
        })
    }
}

impl<F: Float> RealtimePerformanceMonitor<F> {
    pub fn new() -> Result<Self> {
        Ok(Self {
            metrics: HashMap::new(),
            monitoring_frequency: 100,
            alert_thresholds: HashMap::new(),
        })
    }
}

impl<F: Float> ForgettingPreventionSystem<F> {
    /// Assess and prevent forgetting
    pub fn assess_and_prevent(&mut self, _input: &[F], _target: &[F]) -> Result<()> {
        // Placeholder implementation
        Ok(())
    }
}

impl<F: Float> DynamicArchitectureModifier<F> {
    /// Modify architecture
    pub fn modify_architecture(&mut self) -> Result<()> {
        // Placeholder implementation
        Ok(())
    }
}

impl<F: Float> RealtimePerformanceMonitor<F> {
    /// Update metrics
    pub fn update_metrics(&mut self, _input: &[F], _target: &[F]) -> Result<()> {
        // Placeholder implementation
        self.metrics.insert(
            "accuracy".to_string(),
            F::from(0.9).expect("Failed to convert constant to float"),
        );
        Ok(())
    }
}